Using T1 mapping indices to evaluate muscle function and predict conservative treatment outcomes in diabetic patients with peripheral arterial disease.

OBJECTIVES: To investigate interstitial muscle fibrosis via T1 mapping indices and its relationships with muscle function and conservative treatment outcomes. METHODS: A total of 49 DM patients with PAD were prospectively recruited from 2016 to 2017. All PAD patients underwent pre-treatment MRI with conservative treatment via a rehabilitation program and antiplatelet therapy. The need to require percutaneous transluminal angioplasty intervention was recorded as intolerance to conservative treatment outcomes. We quantified calf interstitial muscle fibrosis using T1 mapping indices (native T1, post-contrast T1, and the extracellular volume fraction [ECV]). Muscle function was evaluated using a 6-min walking test (6MWT) and a 3-min stepping test (3MST). PAD patients were divided into two groups according to their tolerance or intolerance of the conservative treatment. Pearson's correlation, reproducibility, and multivariable Cox hazard analyses were performed with p < 0.05 indicating statistical significance. RESULTS: Among the T1 mapping indices in the posterior compartment of the calf in PAD patients, the native T1 value was significantly correlated with 6MWT (r = -0.422, p = 0.010) and 3MST (r = -0.427, p = 0.009). All T1 mapping indices showed excellent intra-observer and inter-observer correlations. ECV was an independent predictor of conservative treatment intolerance (average ECV, hazard ratio: 1.045, 95% confidence interval: 1.011-1.079, p = 0.009). CONCLUSIONS: T1 mapping measurements are reproducible with excellent intra-observer and inter-observer correlations. T1 mapping indices may be predictive of treatment and functional outcomes and carry promise in patient evaluation. TRIAL REGISTRATION: Clinical Trials Identifier: NCT02850432 . KEY POINTS: • T1 mapping measurements of the calf muscles are reproducible with excellent intra-observer and inter-observer correlations (0.98 and 0.95 for anterior and posterior compartment muscle extracellular volume matrix [ECV] measurements, respectively). • ECV is shown to independently predict conservative treatment intolerance. • T1 mapping indices may be predictive of treatment and functional outcomes and carry promise in patient evaluation.

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. METHODS: MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC). RESULTS: The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05). CONCLUSION: Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net. CLINICAL RELEVANCE STATEMENT: The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes. KEY POINTS: • DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.

Lung Cancer in Lung Transplant Recipients: Clinical, Radiologic, and Pathologic Characteristics and Treatment Outcome.

OBJECTIVE: This study aimed to investigate clinical and radiologic characteristics of lung cancer in lung transplant recipients and evaluate the treatment course and prognosis. METHODS: The study included 448 patients who underwent lung transplant between 2005 and 2021. All patients had pretransplant chest computed tomography (CT), 429 patients had posttransplant CT, whereas 19 had no posttransplant CT (median number of posttransplant CT, 6; range, 0-24). Medical records of these patients were reviewed to identify patients who developed lung cancer after lung transplant. Computed tomography and positron emission tomography/CT at the time of lung cancer diagnoses were reviewed to obtain imaging features. Demographics, tumor histology, stages, and survival were compared using Fisher exact test and Wilcoxon rank sum test. RESULTS: Among 448 lung transplant recipients with a median follow-up of 71.3 months after lung transplant, 15 patients (3.3%) developed posttransplant lung cancer (13 unilateral, 2 bilateral; 10 men, 5 women; median age, 63.1 years; median time from transplantation to cancer diagnosis, 3.1 years). Twelve cancers were in native lung, and 3 were in transplanted lung. The incidence of lung cancer was higher in single lung transplant recipients than in bilateral lung transplant recipients (10.3% vs 0.6%, respectively; P < 0.0001). Imaging manifestations varied according to tumor stages. Among 12 patients treated for lung cancer, 2 patients developed posttreatment acute respiratory distress syndrome. The median survival from cancer diagnosis of cancer was 6.2 months. CONCLUSIONS: Posttransplant lung cancer was noted in 3% of lung transplant recipients and was more common in unilateral transplant recipients. The prognosis upon diagnosis was poor with rapid clinical deterioration and serious posttreatment complications.

Exploring the aging process of cognitively healthy adults by analyzing cerebrospinal fluid metabolomics using liquid chromatography-tandem mass spectrometry.

BACKGROUND: During biological aging, significant metabolic dysregulation in the central nervous system may lead to cognitive decline and neurodegeneration. However, the metabolomics of the aging process in cerebrospinal fluid (CSF) has not been thoroughly explored. METHODS: In this cohort study of CSF metabolomics using liquid chromatography-mass spectrometry (LC-MS), fasting CSF samples collected from 92 cognitively unimpaired adults aged 20-87 years without obesity or diabetes were analyzed. RESULTS: We identified 37 metabolites in these CSF samples with significant positive correlations with aging, including cysteine, pantothenic acid, 5-hydroxyindoleacetic acid (5-HIAA), aspartic acid, and glutamate; and two metabolites with negative correlations, asparagine and glycerophosphocholine. The combined alterations of asparagine, cysteine, glycerophosphocholine, pantothenic acid, sucrose, and 5-HIAA showed a superior correlation with aging (AUC = 0.982). These age-correlated changes in CSF metabolites might reflect blood-brain barrier breakdown, neuroinflammation, and mitochondrial dysfunction in the aging brain. We also found sex differences in CSF metabolites with higher levels of taurine and 5-HIAA in women using propensity-matched comparison. CONCLUSIONS: Our LC-MS metabolomics of the aging process in a Taiwanese population revealed several significantly altered CSF metabolites during aging and between the sexes. These metabolic alterations in CSF might provide clues for healthy brain aging and deserve further exploration.

Malonyl-CoA Accumulation as a Compensatory Cytoprotective Mechanism in Cardiac Cells in Response to 7-Ketocholesterol-Induced Growth Retardation.

The major oxidized product of cholesterol, 7-Ketocholesterol (7KCh), causes cellular oxidative damage. In the present study, we investigated the physiological responses of cardiomyocytes to 7KCh. A 7KCh treatment inhibited the growth of cardiac cells and their mitochondrial oxygen consumption. It was accompanied by a compensatory increase in mitochondrial mass and adaptive metabolic remodeling. The application of [U-13C] glucose labeling revealed an increased production of malonyl-CoA but a decreased formation of hydroxymethylglutaryl-coenzyme A (HMG-CoA) in the 7KCh-treated cells. The flux of the tricarboxylic acid (TCA) cycle decreased, while that of anaplerotic reaction increased, suggesting a net conversion of pyruvate to malonyl-CoA. The accumulation of malonyl-CoA inhibited the carnitine palmitoyltransferase-1 (CPT-1) activity, probably accounting for the 7-KCh-induced suppression of ß-oxidation. We further examined the physiological roles of malonyl-CoA accumulation. Treatment with the inhibitor of malonyl-CoA decarboxylase, which increased the intracellular malonyl-CoA level, mitigated the growth inhibitory effect of 7KCh, whereas the treatment with the inhibitor of acetyl-CoA carboxylase, which reduced malonyl-CoA content, aggravated such a growth inhibitory effect. Knockout of malonyl-CoA decarboxylase gene (Mlycd-/-) alleviated the growth inhibitory effect of 7KCh. It was accompanied by improvement of the mitochondrial functions. These findings suggest that the formation of malonyl-CoA may represent a compensatory cytoprotective mechanism to sustain the growth of 7KCh-treated cells.

Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.

OBJECTIVES: To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader. METHODS: This single-centre retrospective study screened 21,150 consecutive body CT studies from September 2018 to February 2019. Pulmonary nodules detected by the DLS on axial CT images but not mentioned in initial radiology reports were flagged. Flagged images were scored by four board-certificated radiologists each with at least 5 years of experience. Nodules with scores of 2 (understandable miss) or 3 (should not be missed) were then categorised as unlikely to be clinically significant (2a or 3a) or likely to be clinically significant (2b or 3b) according to the 2017 Fleischner guidelines for pulmonary nodules. The miss rate was defined as the total number of studies receiving scores of 2 or 3 divided by total screened studies. RESULTS: Among 172 nodules flagged by the DLS, 60 (35%) missed nodules were confirmed by the radiologists. The nodules were further categorised as 2a, 2b, 3a, and 3b in 24, 14, 10, and 12 studies, respectively, with an overall positive predictive value of 35%. Missed pulmonary nodules were identified in 0.3% of all CT images, and one-third of these lesions were considered clinically significant. CONCLUSIONS: Use of DLS-assisted automated detection as a second reader can identify missed pulmonary nodules, some of which may be clinically significant. CLINICAL RELEVANCE/APPLICATION: Use of DLS to help radiologists detect pulmonary lesions may improve patient care. KEY POINTS: • DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort. • Performance of combined radiologists and DLS was better than DLS or radiologists alone. • Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.

Interstitial Lung Abnormalities in Patients With Locally Advanced Esophageal Cancer: Prevalence, Risk Factors, and Clinical Implications.

PURPOSE: Interstitial lung abnormalities (ILAs) represent nondependent abnormalities on chest computed tomography (CT) indicating lung parenchymal damages due to inflammation and fibrosis. Interstitial lung abnormalities have been studied as a predictor of clinical outcome in lung cancer, but not in other thoracic malignancies. The present study investigated the prevalence of ILA in patients with esophageal cancer and identified risk factors and clinical implications of ILA in these patients. METHODS: The study included 208 patients with locally advanced esophageal cancer (median age, 65.6 years; 166 males, 42 females). Interstitial lung abnormality was scored on baseline CT scans before treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA). Clinical characteristics and overall survival were compared in patients with ILA (score 2) and others. RESULTS: An ILA was present in 14 of 208 patients (7%) with esophageal cancer on pretreatment chest CT. Patients with ILA were significantly older (median age, 69 vs 65, respectively; P = 0.011), had a higher number of pack-years of smoking ( P = 0.02), and more commonly had T4 stage disease ( P = 0.026) than patients with ILA score of 1 or 0. Interstitial lung abnormality on baseline scan was associated with a lack of surgical resection after chemoradiotherapy (7/14, 50% vs 39/194, 20% respectively; P = 0.016). Interstitial lung abnormality was not associated with overall survival (log-rank P = 0.75, Cox P = 0.613). CONCLUSIONS: An ILA was present in 7% of esophageal cancer patients, which is similar to the prevalence in general population and in smokers. Interstitial lung abnormality was strongly associated with a lack of surgical resection after chemoradiotherapy, indicating an implication of ILA in treatment selection in these patients, which can be further studied in larger cohorts.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

OBJECTIVE: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). MATERIALS AND METHODS: T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. RESULTS: AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). DISCUSSION: All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s.

Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-13C]Pyruvate.

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in 13C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.

Response to Brigatinib Targeted Therapy in Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Exon 19 Deletion, T790M, and cis-C797S Triple Mutations: A Case Report.

Epidermal growth factor receptor (EGFR) triple mutations with exon 19 deletion (del19), T790M, and cis-C797S (del19/T790M/cis-C797S mutations) frequently occur in patients with non-small cell lung cancer (NSCLC), while progression to frontline EGFR-tyrosine kinase inhibitors (TKIs) and osimertinib was resistant to all clinically available EGFR-TKIs. Brigatinib monotherapy may be a potential treatment for NSCLC harboring del19/T790M/cis-C797S mutations based on preclinical studies; however, no clinical report has evaluated its efficacy on EGFR del19/T790M/cis-C797S mutations. Herein, we present a case of a female patient with EGFR del19-mutated NSCLC treated with afatinib followed by osimertinib due to acquired T790M mutation. The EGFR del19/T790M/cis-C797S mutations were detected following osimertinib treatment. Complete response of skull metastasis was confirmed after brigatinib treatment (90 mg daily). Unfortunately, she experienced intolerable adverse events; therefore, brigatinib was discontinued after three-month usage. This report provides the first reported evidence for the use of brigatinib monotherapy in patients with NSCLC harboring EGFR del19/T790M/cis-C797S mutations after progression to previous EGFR-TKIs.

Metabolomics reveals microbial-derived metabolites associated with immunoglobulin E responses in filaggrin-related atopic dermatitis.

BACKGROUND: Filaggrin (FLG) gene mutation and immunoglobulin E (IgE)-mediated sensitization are the most important predictors of atopic dermatitis (AD). However, a metabolomics-based approach to address the metabolic impact of FLG mutations on allergic IgE responses for AD is still lacking. We, though, determine the relationships of metabolic profiles in AD with FLG mutations and allergic responses. METHODS: Eighty-one children with adolescent AD (n = 58) and healthy controls (n = 23) were prospectively enrolled. Mutations in the filaggrin gene were identified using whole-exome sequencing, and plasma metabolic profiles were determined using 1 H-nuclear magnetic resonance (NMR) spectroscopy. Integrative analyses of their associations related to total serum IgE levels were performed, and further metabolic functional pathways for AD were also assessed. RESULTS: Metabolites contributed to the separation between AD and controls were identified using the supervised partial least squares discriminant analysis (Q2 /R2  = 0.90, Ppermutation <0.001). Nitrogen and amino acid metabolisms for energy production, and microbe-related methane and propanoate metabolisms were significantly associated with AD compared with healthy controls (FDR-adjusted p < .05). Five of fifteen metabolites related to FLG mutations were positively correlated with total serum IgE levels. Among them, dimethylamine and isopropanol were strongly associated with methane metabolism and propanoate metabolism, respectively, in AD with FLG mutations (FDR-adjusted p < .01). CONCLUSION: A strong correlation of microbial-derived metabolites, dimethylamine and isopropanol, with FLG mutations and IgE allergic reactions provides the influence of host genetics on the microbiome to regulate susceptibility to allergic responses in the pathogenesis of AD.

Radiation-Induced Metabolic Shifts in the Hepatic Parenchyma: Findings from 18F-FDG PET Imaging and Tissue NMR Metabolomics in a Mouse Model for Hepatocellular Carcinoma.

PURPOSE: By taking advantage of 18F-FDG PET imaging and tissue nuclear magnetic resonance (NMR) metabolomics, we examined the dynamic metabolic alterations induced by liver irradiation in a mouse model for hepatocellular carcinoma (HCC). METHODS: After orthotopic implantation with the mouse liver cancer BNL cells in the right hepatic lobe, animals were divided into two experimental groups. The first received irradiation (RT) at 15 Gy, while the second (no-RT) did not. Intergroup comparisons over time were performed, in terms of 18F-FDG PET findings, NMR metabolomics results, and the expression of genes involved in inflammation and glucose metabolism. RESULTS: As of day one post-irradiation, mice in the RT group showed an increased 18F-FDG uptake in the right liver parenchyma compared with the no-RT group. However, the difference reached statistical significance only on the third post-irradiation day. NMR metabolomics revealed that glucose concentrations peaked on day one post-irradiation both, in the right and left lobes-the latter reflecting a bystander effect. Increased pyruvate and glutamate levels were also evident in the right liver on the third post-irradiation day. The expression levels of the glucose-6-phosphatase (G6PC) and fructose-1, 6-bisphosphatase 1 (FBP1) genes were down-regulated on the first and third post-irradiation days, respectively. Therefore, liver irradiation was associated with a metabolic shift from an impaired gluconeogenesis to an enhanced glycolysis from the first to the third post-irradiation day. CONCLUSION: Radiation-induced metabolic alterations in the liver parenchyma occur as early as the first post-irradiation day and show dynamic changes over time.

The association between immune-related adverse events and survival outcomes in Asian patients with advanced melanoma receiving anti-PD-1 antibodies.

BACKGROUND: The association between immune-related adverse events (irAEs) and survival outcomes in patients with advanced melanoma receiving therapy with immune checkpoint inhibitors (ICIs) has not been well established, particularly in Asian melanoma. METHODS: We retrospectively reviewed 49 melanoma patients undergoing therapy with ICIs (anti-PD-1 monotherapy), and analyzed the correlation between irAEs and clinical outcomes including progression-free survival (PFS) and overall survival (OS). RESULTS: Overall, the patients who experienced grade 1-2 irAEs had longer PFS (median PFS, 4.6 vs. 2.5 months; HR, 0.52; 95% CI: 0.27-0.98; p = 0.042) and OS (median OS, 15.2 vs. 5.7 months; HR, 0.50; 95% CI: 0.24-1.02; p = 0.058) than the patients who did not experience irAEs. Regarding the type of irAE, the patients with either skin/vitiligo or endocrine irAEs showed better PFS (median PFS, 6.1 vs. 2.7 months; HR, 0.40, 95% CI: 0.21-0.74; p = 0.003) and OS (median OS, 18.7 vs. 4.5 months; HR, 0.34, 95% CI: 0.17-0.69, p = 0.003) than patients without any of these irAEs. CONCLUSIONS: Melanoma patients undergoing anti-PD-1 monotherapy and experiencing mild-to-moderate irAEs (grade 1-2), particularly skin (vitiligo)/endocrine irAEs had favorable survival outcomes. Therefore, the association between irAEs and the clinical outcomes in melanoma patients undergoing anti-PD-1 ICIs may be severity and type dependent.

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

OBJECTIVE: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. METHODS: This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB-IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed. RESULTS: Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (p < 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70-0.99). CONCLUSION: U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings. U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. KEY POINTS: • U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images. • Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization. • First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses.

Response evaluation for immunotherapy through semi-automatic software based on RECIST 1.1, irRC, and iRECIST criteria: comparison with subjective assessment.

BACKGROUND: Pseudoprogression is difficult to diagnose in patients undergoing immunotherapy. Subjective response assessment is still common in clinical practice. PURPOSE: To evaluate the differences between response evaluation criteria in solid tumors version 1.1 (RECIST 1.1), immune-related response criteria (irRC), and modified RECIST 1.1 for immunotherapy (iRECIST) through semi-automatic software, and to compare iRECIST-based response evaluation with subjective assessment. MATERIAL AND METHODS: The best overall response of each patient based on RECIST 1.1, irRC, and iRECIST was determined on CT scans through semi-automatic software and the differences between the criteria were evaluated. Criteria-based response evaluation through semi-automatic software was compared with subjective assessment on radiology report by correlating the best overall response to overall survival. RESULTS: A total of 21 patients were included (five patients with melanoma, 12 patients with non-small-cell lung cancer, and four patients with hepatocellular carcinoma). Two patients with progressive disease by RECIST 1.1 but non-progressive disease by irRC and iRECIST eventually experienced tumor response and had favorable outcomes, indicating pseudoprogression. The survival difference between patients with non-progressive disease and progressive disease was better stratified through iRECIST-based response evaluation (P = 0.078) than that through subjective assessment (P = 0.501). CONCLUSION: Pseudoprogression in immunotherapy may be captured through semi-automatic software utilizing irRC or iRECIST criteria. iRECIST-based response evaluation may provide a better survival stratification compared with subjective assessment.

Serum metabolites may be useful markers to assess vascular invasion and identify normal alpha-fetoprotein in hepatocellular carcinoma undergoing liver resection: a pilot study.

PURPOSE: Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a dismal prognosis. Vascular invasion, among others, is the most robust indicator of postoperative recurrence and overall survival after liver resection for HCC. Few studies to date have attempted to search for effective markers to predict vascular invasion before the operation. The current study would examine the plasma metabolic profiling via 1H-NMR of HCC patients undergoing liver resection and aim to search for potential biomarkers in the early detection of HCC with normal alpha-fetoprotein (AFP) and the diagnosis of vascular invasion preoperatively. MATERIALS AND METHODS: HCC patients scheduled to receive liver resections for their HCC were recruited and divided into two separate groups, investigation cohort and validation cohort. Their preoperative blood samples were collected and subjected to a comprehensive metabolomic profiling using 1H-nuclear magnetic resonance spectroscopy (NMR). RESULTS: There were 35 HCC patients in the investigation group and 22 patients in the validation group. Chronic hepatitis B remained the most common etiology of HCC, followed by chronic HCV infection. The two study cohorts were essentially comparable in terms of major clinicopathological variables. After 1H-nuclear NMR analysis, we found in the investigation cohort that HCC with normal alpha-fetoprotein (AFP < 15 ng/mL) had significantly higher serum level of O-acetylcarnitine than those with higher AFP (AFP ≥ 15 ng/mL, P = 0.025). In addition, HCC with microscopic vascular invasion (VI) had significantly higher preoperative serum level of formate than HCC without microscopic VI (P = 0.023). These findings were similar in the validation cohort. CONCLUSION: A comprehensive metabolomic profiling of HCC demonstrated that serum metabolites may be utilized to assist the early diagnosis of AFP-negative HCC patients and recognition of microvascular invasion in order to facilitate preoperative surgical planning and postoperative follow-up. Further, larger scale prospective studies are warranted to consolidate our findings.

Management and clinical outcomes of patients with recurrent/progressive ovarian clear cell carcinoma.

BACKGROUND/PURPOSE: Ovarian clear cell carcinoma (OCCC) with recurrence/progression after treatment has dismal prognosis. We aimed to investigate the management and outcomes of such patients. METHODS: OCCC patients who were treated between 2000 and 2013 with cancer recurrence or progression after primary treatment were analyzed. Univariate and multivariate analyses were used to identify the independent predictors of survival after recurrence (SAR) and cancer-specific survival (CSS). RESULTS: A total of 64 patients experienced treatment failure (49 recurred after remission and 15 progressed without remission). The 5-year CSS rates of recurrent/progressive OCCC patients were 22.9% (progression group: median CSS 5.9 months [range, 0.8-25.2] vs recurrence group: 43.6 months [range, 7.1-217.8]; p < 0.001). Patients with solitary recurrence had significantly better SAR than those with disseminated relapse (median: not reached vs 10.4 months, p < 0.001). On multivariate analysis, six models each for SAR and CSS were formulated alternatively including highly correlated variables for the recurrence group. Of these models, solitary relapse pattern (HR: 0.07, p < 0.001), progression-free interval (PFI) > 12 months (HR: 0.22-0.40, p = 0.001 and p = 0.023), CA125 < 35 U/mL at initial recurrence (HR: 0.32, p = 0.007), and overall salvage treatment including radiotherapy (HR: 0.19, p = 0.001) were significant predictors of favorable SAR. The same significant predictors were selected for CSS. CONCLUSION: Recurrent OCCC can be treated with curative intent if the relapse is solitary and can be completely resected or encompassed with radiotherapy, whereas novel therapies are needed for disseminated relapse or progression during primary treatment.

Metabolomics Reveals Anaerobic Bacterial Fermentation and Hypoxanthine Accumulation for Fibrinous Pleural Effusions in Children with Pneumonia.

Fibrin formation in infectious parapneumonic effusion (IPE) characterizes complicated parapneumonic effusion and is important for providing guidelines for the management of IPEs that require aggressive interventions. We aim to identify metabolic mechanisms associated with bacterial invasion, inflammatory cytokines, and biochemical markers in cases of fibrinous infectious pleural effusions in children with pneumonia. Pleural fluid metabolites were determined by 1H nuclear magnetic resonance spectroscopy. Metabolites that contributed to the separation between fibrinous and nonfibrinous IPEs were identified using supervised partial least squares discriminant analysis ( Q2/ R2 = 0.84; Ppermutation < 0.01). IL-1ß in the inflammatory cytokines and glucose in the biochemical markers were significantly correlated with 11 and 9 pleural fluid metabolites, respectively, and exhibited significant overlaps. Four metabolites, including glucose, lactic acid, 3-hydroxybutyric acid, and hypoxanthine, were significantly correlated with plasminogen activator inhibitor type 1 in the fibrinolytic system enzymes. Metabolic pathway analysis revealed that anaerobic bacterial fermentation with increased lactic acid and butyric acid via glucose consumption and adenosine triphosphate hydrolysis with increased hypoxanthine appeared to be associated with fibrinous IPE. Our results demonstrate that an increase in lactic acid anaerobic fermentation and hypoxanthine accumulation under hypoxic conditions are associated with fibrin formation in IPE, representing advanced pleural inflammatory progress in children with pneumonia.

Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy.

INTRODUCTION: Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers. OBJECTIVES: We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy. METHODS: We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. RESULTS: A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively. CONCLUSION: Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.

Gut microbial-derived butyrate is inversely associated with IgE responses to allergens in childhood asthma.

BACKGROUND: A comprehensive metabolomics-based approach to address the impact of specific gut microbiota on allergen sensitization for childhood rhinitis and asthma is still lacking. METHODS: Eighty-five children with rhinitis (n = 27) and with asthma (n = 34) and healthy controls (n = 24) were enrolled. Fecal metabolomic analysis with 1 H-nuclear magnetic resonance (NMR) spectroscopy and microbiome composition analysis by bacterial 16S rRNA sequencing were performed. An integrative analysis of their associations with allergen-specific IgE levels for allergic rhinitis and asthma was also assessed. RESULTS: Amino acid, ß-alanine, and butanoate were the predominant metabolic pathways in the gut. Among them, amino acid metabolism was negatively correlated with the phylum Firmicutes, which was significantly reduced in children with rhinitis and asthma. Levels of histidine and butyrate metabolites were significantly reduced in children with rhinitis (P = 0.029) and asthma (P = 0.009), respectively. In children with asthma, a reduction in butyrate-producing bacteria, including Faecalibacterium and Roseburia spp., and an increase in Clostridium spp. were negatively correlated with fecal amino acids and butyrate, respectively (P < 0.01). Increased Escherichia spp. accompanied by increased ß-alanine and 4-hydroxybutyrate appeared to reduce butyrate production. Low fecal butyrate was significantly associated with increased total serum and mite allergen-specific IgE levels in children with asthma (P < 0.05). CONCLUSION: A reduced fecal butyrate is associated with increased mite-specific IgE levels and the risk of asthma in early childhood. Fecal ß-alanine could be a specific biomarker connecting the metabolic dysbiosis of gut microbiota, Clostridium and Escherichia spp., in childhood asthma.